Development and usability testing of tools to facilitate incorporating intersectionality in knowledge translation
Bibliographic record
Abstract
BACKGROUND: The field of knowledge translation (KT) has been criticized for neglecting contextual and social considerations that influence health equity. Intersectionality, a concept introduced by Black feminist scholars, emphasizes how human experience is shaped by combinations of social factors (e.g., ethnicity, gender) embedded in systemic power structures. Its use has the potential to advance equity considerations in KT. Our objective was to develop and conduct usability testing of tools to support integrating intersectionality in KT through three key phases of KT: identifying the gap; assessing barriers to knowledge use; and selecting, tailoring, and implementing interventions. METHODS: We used an integrated KT approach and assembled an interdisciplinary development committee who drafted tools. We used a mixed methods approach for usability testing with KT intervention developers that included semi-structured interviews and the System Usability Scale (SUS). We calculated an average SUS score for each tool. We coded interview data using the framework method focusing on actionable feedback. The development committee used the feedback to revise tools, which were formatted by a graphic designer. RESULTS: Nine people working in Canada joined the development committee. They drafted an intersectionality primer and one tool that included recommendations, activities, reflection prompts, and resources for each of the three implementation phases. Thirty-one KT intervention developers from three countries participated in usability testing. They suggested the tools to be shorter, contain more visualizations, and use less jargon. Average SUS scores of the draft tools ranged between 60 and 78/100. The development committee revised and shortened all tools, and added two, one-page summary documents. The final toolkit included six documents. CONCLUSIONS: We developed and evaluated tools to help embed intersectionality considerations in KT. These tools go beyond recommending the use of intersectionality to providing practical guidance on how to do this. Future work should develop guidance for enhancing social justice in intersectionality-enhanced KT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".